The introduction of genomics has been instrumental in accelerating the pace of drug discovery. The genomic technologies have proved their value in finding novel drug targets. Further improvement in this area will provide more efficient tools resulting in faster and more cost efficient development of potential drugs.
The drug discovery process includes several steps: the identification of a potential biochemical target associated with disease, screening for active compounds and further chemical design, preclinical tests, and finally clinical trials. The efficiency of this process is still far from perfect: it is estimated that about 75% of money spent in the R&D process went to fund failed projects. Moreover, the later in the product development a failure occurs, the bigger are losses associated with this project. Therefore, there is a need for early elimination of future failures to considerably cut costs of the whole drug development process. Thus, the quality of the original molecular target becomes a decisive factor for cost-effective drug development.
One approach that promises to impact on the process of target identification and validation is transcription profiling. This method compares expression of genes in a specific situation: for example, between disease and normal cells, between control and drug-treated cells or between cells responding to treatment and those resistant to it. The information generated by this approach may directly identify specific genes to be targeted by a therapy, and, importantly, reveals biochemical pathways involved in disease and treatment. In brief, it not only provides biochemical targets, but at the same time, a way to assess the quality of these targets. Moreover, in combination with cell-based screening, transcription profiling is positioned to dramatically change the field of drug discovery. Historically, screening for a potential drug was successfully performed using phenotypic change as a marker in functional cellular system. For example, growth of tumor cells in culture was monitored to identify anticancer drugs. Similarly, bacterial viability was used in assays aimed at identifying antibiotic compounds. Such screens were typically conducted without prior knowledge of the targeted biochemical pathway. In fact, the identified effective compounds revealed such pathways and pointed out the true molecular target, enabling subsequent rational design of the next generations of drugs.
Modern tools of transcription profiling can be used to design novel screening methods that will utilize gene expression in place of phenotypic changes to assess effectiveness of a drug. For example, these methods are described in U.S. Pat. Nos. 5,262,311; 5,665,547; 5,599,672; 5,580,726; 6,045,988 and 5,994,076, as well as Luehrsen et al. (1997, Biotechniques, 22:168-74; Liang and Pardee (1998, Mol Biotechnol. 10:261-7). Such approach will be invaluable for drug discovery in the field of central nervous system (CNS) disorders such as dementia, mild cognitive impairment, depression, etc., where phenotypic screening is inapplicable, but desired transcription profile can be readily established and linked to particular disorders. Once again, the identified effective compounds will reveal the underlying molecular processes. In addition, this method can be instrumental for development of improved versions of existent drugs, which act at several biochemical targets at the same time to generate the desired pharmacological effect. In such case the change in the transcriptional response may be a better marker for drug action than selection based on optimization of binding to multiple targets.
Prior to the instant invention, the most advanced method of transcription profiling is based on technology using DNA microarrays, for example, as reviewed in Greenberg, 2001 Neurology 57:755-61; Wu, 2001, J Pathol. 195:53-65; Dhiman et al., 2001, Vaccine 20:22-30; Bier et al., 2001 Fresenius J Anal Chem. 371:151-6; Mills et al., 2001, Nat Cell Biol. 3:E175-8; and as described in U.S. Pat. Nos. 5,593,839; 5,837,832; 5,856,101; 6,203,989; 6,271,957; and 6,287,778. DNA microarray is a method which performs simultaneous comparison of the expression of several thousand genes in a given sample by assessing hybridization of the labeled polynucleotide samples, obtained by reverse transcription of mRNAs, to the DNA molecules attached to the surface of the test array. While the technology provides valuable information about transcriptional changes, it is far from perfect.
First of all, this technology is limited to the pool of genes presented in the microarray. The current printing methods allows placement of 10,000-15,000 genes on a single chip, which is essentially a number of genes expressed in a particular cell type. Given the diversity of cell types, it requires development of specific arrays for specific cell types. While theoretically possible, this task is hard to acheive, since it requires knowledge about gene pool expressed in these cells prior to microarray manufacturing.
Moreover, the number of transcripts in a tissue sample is even higher than in a cellular sample and exceeds the current capacity of the microarray. In addition, some changes in gene expression result from alternative splicing, which further increases the number of transcripts that need to be assessed. The only possibility to overcome these difficulties will be to develop multiple arrays that will cover the entire genome, including alternatively spliced genes. This approach will significantly increase the cost of a single experiment and will require a large biological sample, perhaps larger than is reasonably available.
Secondly, at present, DNA microarrays do not provide quantitatively accurate data, and observed changes in gene expression have to be confirmed by an independent methods, for example, quantitative PCR (Q-PCR).
In addition, a typical microarray experiment includes several manual steps which affect the reproducibility of this method.
And finally, the expression of rare transcripts, which may be of particular interest, can not be accurately measured by microarrays using current detection techniques. These limitations demonstrate a need to develop alternative methods to perform transcription profiling, preferably one that 1) will not require prior knowledge of the sequences of the expressed gene pool before the assay but by itself will provide this information during/after the assay; 2) will measure quantitative changes in the level of expressed transcripts; 3) will be able to detect expression of rare genes; and 4) can be automated.
Capillary electrophoresis has been used to quantitatively detect gene expression. Rajevic at el. (2001, Pflugers Arch. 442(6 Suppl 1):R190-2) discloses a method for detecting differential expression of oncogenes by using seven pairs of primers for detecting the differences in expression of a number of oncogenes simultaneously. Sense primers were 5′ end-labelled with a fluorescent dye. Multiplex fluorescent RT-PCR results were analyzed by capillary electrophoresis on ABI-PRISM 310 Genetic Analyzer. Borson et al. (1998, Biotechniques 25:130-7) describes a strategy for dependable quantitation of low-abundance mRNA transcripts based on quantitative competitive reverse transcription PCR (QC-RT-PCR) coupled to capillary electrophoresis (CE) for rapid separation and detection of products. George et al., (1997, J Chromatogr B Biomed Sci Appl 695:93-102) describes the application of a capillary electrophoresis system (ABI 310) to the identification of fluorescent differential display generated EST patterns. Odin et al. (1999, J Chromatogr B Biomed Sci Appl 734:47-53) describes an automated capillary gel electrophoresis with multicolor detection for separation and quantification of PCR-amplified cDNA.
Omori et al. (2000, Genomics 67:140-5) measures and compares the amount of commercially purchased α-globin mRNA by competitive PCR in two independently reverse transcribed cDNA samples using oligo(dT) or oligo(dU) primers. The oligo(dT) or oligo(dU) primers share a 3′ oligo(dT) or oligo(dU) sequence and a 5′ common sequence. In addition the oligo(dT) or oligo(dU) primer for each sample also contains a unique 29 nucleotide sequence between the 3′ oligo(dT) or oligo(dU) sequence and the 5′ common sequence. After the synthesis of first strand cDNA, PCR is performed to amplify the cDNA using a gene-specific primer and a primer complementary to the common sequence which is labeled with a unique label. The amplified PCR products are then analyzed by spotting onto a detection plate of a fluorescence scanner.
There is a need in the art for simple, sensitive method for simultaneous quantitative detection of gene expression profile in multiple samples.